Enhancing EEG-Based Authentication With Transformer in Internet of Things

With the rapid growth of Internet of Things (IoT) and edge computing platforms, the Internet of Medical Things (IoMT) has become popular and important in healthcare industry, i.e., there is an increase of brainwave headsets and headbands. However, the security and privacy of shared data can be easily compromised if an attacker can access the IoMT devices and check all the data. There is a need to authenticate users before they can use the healthcare devices. For this reason, Electroencephalography (EEG) based authentication is a necessary security solution. In recent years, EEG-based authentication has witnessed significant advancements, but traditional models face challenges in capturing the complex spatial and temporal dependencies present in EEG signals. This work aims to address these limitations and explore the effect of Transformer model in the domain of EEG-based authentication. In particular, we devise a modified Vision Transformer model (ViT) to handle the specific characteristics of EEG data, such as spatial and temporal dependencies. In the evaluation, we compare our approach with the similar methods in the literature and examine the effect of fine-tune based on two datasets. The results demonstrate that our approach can effectively capture long-range dependencies and outperform conventional models.